
where \(\hat{u} :={u(x,t)}\)  refers to snapshot data at time step \(t\) . The choice of the reward function can have a significant effect on the performance of the agent [1]} and needs to be carefully designed depending upon the problem. The performance of the MMRL approach is compared against the linear-modal RL (LMRL) approach where the agent selects only a scalar value of eddy viscosity amplitude as an action and linear viscosity kernel [2]} is utilized to assign the modal eddy viscosity coefficients. Specifically, in LMRL approach we have \(\eta _k(t)=\eta _e(t)(k/R)\) , where \(\eta _e\)  is the eddy viscosity amplitude selected by the agent as an action.
